Transition from a predictive multiple linear regression model to an explanatory simple nonlinear regression model with higher level of prediction: A systems dynamics approach
One of the main assumptions of the linear regression analysis is the existence of a causal relationship between the variables analyzed, which the regression analysis does not demonstrate. This paper demonstrates the causality between the variables analyzed through the construction and analysis of t...
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| Format: | Article |
| Language: | English |
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Universidad de Antioquia
2014-02-01
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| Series: | Revista Facultad de Ingeniería Universidad de Antioquia |
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| Online Access: | https://revistas.udea.edu.co/index.php/ingenieria/article/view/14469 |
| _version_ | 1797861725219848192 |
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| author | Roberto Baeza-Serrato José Antonio Vázquez-López |
| author_facet | Roberto Baeza-Serrato José Antonio Vázquez-López |
| author_sort | Roberto Baeza-Serrato |
| collection | DOAJ |
| description |
One of the main assumptions of the linear regression analysis is the existence of a causal relationship between the variables analyzed, which the regression analysis does not demonstrate. This paper demonstrates the causality between the variables analyzed through the construction and analysis of the feedback from the variables under study, expressed in a causal diagram and validated through dynamic simulation. The major contribution of this research is the proposal of the use of the system dynamics approach to develop a method of transition from a multiple regression predictive model to a simpler nonlinear regression explanatory model, which increases the level of prediction of the model. The mean square error (MSE) is taken as a criterion for prediction. The validation in the transition model was performed with three linear regression models obtained experimentally in a textile company, showing a method for increasing the reliability of prediction models.
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| first_indexed | 2024-04-09T22:08:50Z |
| format | Article |
| id | doaj.art-e9eb8e4328a8483db23273c19f6bcfa3 |
| institution | Directory Open Access Journal |
| issn | 0120-6230 2422-2844 |
| language | English |
| last_indexed | 2024-04-09T22:08:50Z |
| publishDate | 2014-02-01 |
| publisher | Universidad de Antioquia |
| record_format | Article |
| series | Revista Facultad de Ingeniería Universidad de Antioquia |
| spelling | doaj.art-e9eb8e4328a8483db23273c19f6bcfa32023-03-23T12:33:17ZengUniversidad de AntioquiaRevista Facultad de Ingeniería Universidad de Antioquia0120-62302422-28442014-02-01717110.17533/udea.redin.14469Transition from a predictive multiple linear regression model to an explanatory simple nonlinear regression model with higher level of prediction: A systems dynamics approachRoberto Baeza-Serrato0José Antonio Vázquez-López1Superior Technological Institute of the South of GuanajuatoTechnological Institute of Celaya One of the main assumptions of the linear regression analysis is the existence of a causal relationship between the variables analyzed, which the regression analysis does not demonstrate. This paper demonstrates the causality between the variables analyzed through the construction and analysis of the feedback from the variables under study, expressed in a causal diagram and validated through dynamic simulation. The major contribution of this research is the proposal of the use of the system dynamics approach to develop a method of transition from a multiple regression predictive model to a simpler nonlinear regression explanatory model, which increases the level of prediction of the model. The mean square error (MSE) is taken as a criterion for prediction. The validation in the transition model was performed with three linear regression models obtained experimentally in a textile company, showing a method for increasing the reliability of prediction models. https://revistas.udea.edu.co/index.php/ingenieria/article/view/14469system dynamicscausalitymodel predictiveexplanatory modelmean square errorlinear regression |
| spellingShingle | Roberto Baeza-Serrato José Antonio Vázquez-López Transition from a predictive multiple linear regression model to an explanatory simple nonlinear regression model with higher level of prediction: A systems dynamics approach Revista Facultad de Ingeniería Universidad de Antioquia system dynamics causality model predictive explanatory model mean square error linear regression |
| title | Transition from a predictive multiple linear regression model to an explanatory simple nonlinear regression model with higher level of prediction: A systems dynamics approach |
| title_full | Transition from a predictive multiple linear regression model to an explanatory simple nonlinear regression model with higher level of prediction: A systems dynamics approach |
| title_fullStr | Transition from a predictive multiple linear regression model to an explanatory simple nonlinear regression model with higher level of prediction: A systems dynamics approach |
| title_full_unstemmed | Transition from a predictive multiple linear regression model to an explanatory simple nonlinear regression model with higher level of prediction: A systems dynamics approach |
| title_short | Transition from a predictive multiple linear regression model to an explanatory simple nonlinear regression model with higher level of prediction: A systems dynamics approach |
| title_sort | transition from a predictive multiple linear regression model to an explanatory simple nonlinear regression model with higher level of prediction a systems dynamics approach |
| topic | system dynamics causality model predictive explanatory model mean square error linear regression |
| url | https://revistas.udea.edu.co/index.php/ingenieria/article/view/14469 |
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